DOMAIN: Health Care
CONTEXT: Computer vision can be used in health care for identifying diseases. In Pneumonia detection we need to detect Inflammation of the lungs. In this challenge, you’re required to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, your algorithm needs to automatically locate lung opacities on chest radiographs.
DATA DESCRIPTION:
- In the dataset, some of the features are labeled “Not Normal No Lung Opacity”. This extra third class indicates that while pneumonia was determined not to be present, there was nonetheless some type of abnormality on the image and oftentimes this finding may mimic the appearance of true pneumonia. Dicom original images: - Medical images are stored in a special format called DICOM files (*.dcm). They contain a combination of header metadata as well as underlying raw image arrays for pixel data.
- Dataset has been attached along with this project. Please use the same for this capstone project.
- Original link to the dataset : https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data [ for your reference only ]. You can refer to the details of the dataset in the above link
- Acknowledgements: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/overview/acknowledgements.
PROJECT OBJECTIVE: Design a DL based algorithm for detecting pneumonia.
Milestone 1
importing important libs
In [ ]:
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import math
import keras
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,confusion_matrix
import cv2
import pickle
import csv
import random
from skimage import io
from skimage import measure
from skimage.transform import resize
import tensorflow as tf
from tensorflow import keras
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
from sklearn import metrics
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, Callback
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Reshape, Concatenate, Flatten, MaxPooling2D, BatchNormalization
from tensorflow.keras.layers import Dense, Dropout, Flatten, Input,GlobalAveragePooling2D
from tensorflow.keras.utils import Sequence
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras import optimizers
from tensorflow.keras.applications.densenet import DenseNet121
from tensorflow.keras.layers import GlobalMaxPooling2D
!pip install pydicom
import pydicom as dcm
from pydicom.pixel_data_handlers.util import apply_voi_lut
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting pydicom
Downloading pydicom-2.3.1-py3-none-any.whl (2.0 MB)
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Installing collected packages: pydicom
Successfully installed pydicom-2.3.1
In [ ]:
!sudo echo -ne '\n' | sudo add-apt-repository ppa:alessandro-strada/ppa >/dev/null 2>&1 # note: >/dev/null 2>&1 is used to supress printing
!sudo apt update >/dev/null 2>&1
!sudo apt install google-drive-ocamlfuse >/dev/null 2>&1
!google-drive-ocamlfuse
!sudo apt-get install w3m >/dev/null 2>&1 # to act as web browser
!xdg-settings set default-web-browser w3m.desktop >/dev/null 2>&1 # to set default browser
%cd /content
!mkdir gdrive
%cd gdrive
!mkdir "My Drive"
!google-drive-ocamlfuse "/content/gdrive/My Drive"
/usr/bin/xdg-open: 851: /usr/bin/xdg-open: www-browser: not found
/usr/bin/xdg-open: 851: /usr/bin/xdg-open: links2: not found
/usr/bin/xdg-open: 851: /usr/bin/xdg-open: elinks: not found
/usr/bin/xdg-open: 851: /usr/bin/xdg-open: links: not found
/usr/bin/xdg-open: 851: /usr/bin/xdg-open: lynx: not found
/usr/bin/xdg-open: 851: /usr/bin/xdg-open: w3m: not found
xdg-open: no method available for opening 'https://accounts.google.com/o/oauth2/auth?client_id=564921029129.apps.googleusercontent.com&redirect_uri=https%3A%2F%2Fgd-ocaml-auth.appspot.com%2Foauth2callback&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force&state=cwhkrc2g1A2phqi%2FZiCt9EwDeqb-JDkJSCX5GeWKOFs'
/bin/sh: 1: firefox: not found
/bin/sh: 1: google-chrome: not found
/bin/sh: 1: chromium-browser: not found
/bin/sh: 1: open: not found
Cannot retrieve auth tokens.
Failure("Error opening URL:https://accounts.google.com/o/oauth2/auth?client_id=564921029129.apps.googleusercontent.com&redirect_uri=https%3A%2F%2Fgd-ocaml-auth.appspot.com%2Foauth2callback&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force&state=cwhkrc2g1A2phqi%2FZiCt9EwDeqb-JDkJSCX5GeWKOFs")
/content
/content/gdrive
Access token retrieved correctly.
Step 1: Import the data. [ 3 points ]
In [ ]:
train_labels = pd.read_csv("/content/gdrive/My Drive/cv capstone/stage_2_train_labels.csv")
print(train_labels.shape)
print(train_labels.head())
detailed_class_info = pd.read_csv("/content/gdrive/My Drive/cv capstone/stage_2_detailed_class_info.csv")
print(detailed_class_info.shape)
print(detailed_class_info.head())
(30227, 6)
patientId x y width height Target
0 0004cfab-14fd-4e49-80ba-63a80b6bddd6 NaN NaN NaN NaN 0
1 00313ee0-9eaa-42f4-b0ab-c148ed3241cd NaN NaN NaN NaN 0
2 00322d4d-1c29-4943-afc9-b6754be640eb NaN NaN NaN NaN 0
3 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 NaN NaN NaN NaN 0
4 00436515-870c-4b36-a041-de91049b9ab4 264.0 152.0 213.0 379.0 1
(30227, 2)
patientId class
0 0004cfab-14fd-4e49-80ba-63a80b6bddd6 No Lung Opacity / Not Normal
1 00313ee0-9eaa-42f4-b0ab-c148ed3241cd No Lung Opacity / Not Normal
2 00322d4d-1c29-4943-afc9-b6754be640eb No Lung Opacity / Not Normal
3 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 Normal
4 00436515-870c-4b36-a041-de91049b9ab4 Lung Opacity
In [ ]:
detailed_class_info.isna().sum()
len(detailed_class_info['patientId'].unique())
Out[ ]:
26684
In [ ]:
train_labels.isna().sum()
len(train_labels['patientId'].unique())
Out[ ]:
26684
In [ ]:
print('Total number of null values in bounding boxes columns(x, y, width, height): {}'.format(train_labels[['x', 'y', 'width', 'height']].isnull().sum().to_dict()))
print('Total value counts for the Target column: {}'.format(train_labels['Target'].value_counts().to_dict()))
Total number of null values in bounding boxes columns(x, y, width, height): {'x': 20672, 'y': 20672, 'width': 20672, 'height': 20672}
Total value counts for the Target column: {0: 20672, 1: 9555}
Observation: Total number of null values in bounding boxes columns is equal to total number of 0s in Target column.
In [ ]:
#Analysing the labels dataframe
train_labels.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 30227 entries, 0 to 30226 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30227 non-null object 1 x 9555 non-null float64 2 y 9555 non-null float64 3 width 9555 non-null float64 4 height 9555 non-null float64 5 Target 30227 non-null int64 dtypes: float64(4), int64(1), object(1) memory usage: 1.4+ MB
In [ ]:
#Checking for unique patient records ==total number of patients
train_labels.patientId.nunique()
Out[ ]:
26684
In [ ]:
train_labels["patientId"].count()-train_labels.patientId.nunique()
Out[ ]:
3543
Observation: From the above data, we can notice that there are around 30227 rows in stage_2_train_labels.csv but we have a total of 26684 unique patientIDs. Hence 3543 patients have more than one bounding box. Verifying this for a patient with the patientId: 00436515-870c-4b36-a041-de91049b9ab4 is the same from 4th and 5th rows of the dataframe.
In [ ]:
train_labels[train_labels.patientId=='00436515-870c-4b36-a041-de91049b9ab4']
Out[ ]:
| patientId | x | y | width | height | Target | |
|---|---|---|---|---|---|---|
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 |
| 5 | 00436515-870c-4b36-a041-de91049b9ab4 | 562.0 | 152.0 | 256.0 | 453.0 | 1 |
In [ ]:
sns.countplot(x='Target',data=train_labels);
In [ ]:
detailed_class_info.head()
Out[ ]:
| patientId | class | |
|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | No Lung Opacity / Not Normal |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | No Lung Opacity / Not Normal |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | No Lung Opacity / Not Normal |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | Normal |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | Lung Opacity |
Observation: There are 3 different class values - Normal,Lung Opacity and No Lung Opacity/Not Normal.
In [ ]:
detailed_class_info.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 30227 entries, 0 to 30226 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30227 non-null object 1 class 30227 non-null object dtypes: object(2) memory usage: 472.4+ KB
In [ ]:
detailed_class_info.describe()
Out[ ]:
| patientId | class | |
|---|---|---|
| count | 30227 | 30227 |
| unique | 26684 | 3 |
| top | 3239951b-6211-4290-b237-3d9ad17176db | No Lung Opacity / Not Normal |
| freq | 4 | 11821 |
In [ ]:
#Checking for duplicate values
detailed_class_info[detailed_class_info.duplicated()].shape
Out[ ]:
(3543, 2)
Observation: The total number of patients match with the value from training labels which is 26684.
In [ ]:
#Checking the class values distribution
sns.countplot(x='class',data=detailed_class_info);
Observation: From the countplot, it is very much noticeable that all the classes are almost equally distributed. We can further check for duplicate and missing values as well
In [ ]:
#Checking for missing values
detailed_class_info.isnull().sum()
Out[ ]:
patientId 0 class 0 dtype: int64
Observation: There are no missing values in the class info data
Step 2: Map training and testing images to its classes. [ 4 points ]
In [ ]:
# Conctinating the two dataset - 'train_labels' and 'detailed_class_info':
train = pd.concat([train_labels,detailed_class_info['class']], axis = 1)
train.head()
Out[ ]:
| patientId | x | y | width | height | Target | class | |
|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity |
In [ ]:
print(train.isna().sum())
print(len(train['patientId'].unique()))
patientId 0 x 20672 y 20672 width 20672 height 20672 Target 0 class 0 dtype: int64 26684
In [ ]:
print(train[train['Target'] == 0].isna().sum())
patientId 0 x 20672 y 20672 width 20672 height 20672 Target 0 class 0 dtype: int64
In [ ]:
print(train[train['class'] == 'Normal'].isna().sum())
print(train[train['class'] == 'No Lung Opacity / Not Normal'].isna().sum())
print(train[train['class'] == 'Lung Opacity'].isna().sum())
patientId 0 x 8851 y 8851 width 8851 height 8851 Target 0 class 0 dtype: int64 patientId 0 x 11821 y 11821 width 11821 height 11821 Target 0 class 0 dtype: int64 patientId 0 x 0 y 0 width 0 height 0 Target 0 class 0 dtype: int64
In [ ]:
train[['x', 'y', 'width', 'height']].isnull().sum().to_dict()
Out[ ]:
{'x': 20672, 'y': 20672, 'width': 20672, 'height': 20672}
Observation: Total number of null values for "Normal" and "No Lung Opacity / Not Normal" is equal to total number of 0s in Target column i.e., who do not have pnemonia have their bounding box columns as NaN
Step 3: Map training and testing images to its annotations [ 4 points ]
In [ ]:
dcm_image_data = dcm.read_file('/content/gdrive/My Drive/cv capstone/stage_2_train_images/c1edf42b-5958-47ff-a1e7-4f23d99583ba.dcm')
print(dcm_image_data)
Dataset.file_meta ------------------------------- (0002, 0000) File Meta Information Group Length UL: 200 (0002, 0001) File Meta Information Version OB: b'\x00\x01' (0002, 0002) Media Storage SOP Class UID UI: Secondary Capture Image Storage (0002, 0003) Media Storage SOP Instance UID UI: 1.2.276.0.7230010.3.1.4.8323329.22672.1517874443.53337 (0002, 0010) Transfer Syntax UID UI: JPEG Baseline (Process 1) (0002, 0012) Implementation Class UID UI: 1.2.276.0.7230010.3.0.3.6.0 (0002, 0013) Implementation Version Name SH: 'OFFIS_DCMTK_360' ------------------------------------------------- (0008, 0005) Specific Character Set CS: 'ISO_IR 100' (0008, 0016) SOP Class UID UI: Secondary Capture Image Storage (0008, 0018) SOP Instance UID UI: 1.2.276.0.7230010.3.1.4.8323329.22672.1517874443.53337 (0008, 0020) Study Date DA: '19010101' (0008, 0030) Study Time TM: '000000.00' (0008, 0050) Accession Number SH: '' (0008, 0060) Modality CS: 'CR' (0008, 0064) Conversion Type CS: 'WSD' (0008, 0090) Referring Physician's Name PN: '' (0008, 103e) Series Description LO: 'view: PA' (0010, 0010) Patient's Name PN: 'c1edf42b-5958-47ff-a1e7-4f23d99583ba' (0010, 0020) Patient ID LO: 'c1edf42b-5958-47ff-a1e7-4f23d99583ba' (0010, 0030) Patient's Birth Date DA: '' (0010, 0040) Patient's Sex CS: 'F' (0010, 1010) Patient's Age AS: '46' (0018, 0015) Body Part Examined CS: 'CHEST' (0018, 5101) View Position CS: 'PA' (0020, 000d) Study Instance UID UI: 1.2.276.0.7230010.3.1.2.8323329.22672.1517874443.53336 (0020, 000e) Series Instance UID UI: 1.2.276.0.7230010.3.1.3.8323329.22672.1517874443.53335 (0020, 0010) Study ID SH: '' (0020, 0011) Series Number IS: '1' (0020, 0013) Instance Number IS: '1' (0020, 0020) Patient Orientation CS: '' (0028, 0002) Samples per Pixel US: 1 (0028, 0004) Photometric Interpretation CS: 'MONOCHROME2' (0028, 0010) Rows US: 1024 (0028, 0011) Columns US: 1024 (0028, 0030) Pixel Spacing DS: [0.14300000000000002, 0.14300000000000002] (0028, 0100) Bits Allocated US: 8 (0028, 0101) Bits Stored US: 8 (0028, 0102) High Bit US: 7 (0028, 0103) Pixel Representation US: 0 (0028, 2110) Lossy Image Compression CS: '01' (0028, 2114) Lossy Image Compression Method CS: 'ISO_10918_1' (7fe0, 0010) Pixel Data OB: Array of 151784 elements
Observation
- Images are stored in DICOM(Digital Imaging and Communications in Medicine) format with .dcm extension. This DICOM image contains more information like patient age, sex, modality, view position and body part and so on.
- pydicom library is used to rread images
In [ ]:
dcm_image_data = dcm.dcmread('/content/gdrive/My Drive/cv capstone/stage_2_train_images/c1edf42b-5958-47ff-a1e7-4f23d99583ba.dcm').pixel_array
dcm_image_data .shape
Out[ ]:
(1024, 1024)
Observation: Dimension of images are 1024x1024. It is very large to process so we will reduce it during model training.
In [ ]:
columns = ['Modality', 'Age', 'Sex']
train_path = os.listdir('/content/gdrive/My Drive/cv capstone/stage_2_train_images/')
class_metadata = train
for col in columns:
class_metadata[col] = None
for index,file_name in enumerate(train_path):
print('processing:',index,file_name)
image_data = dcm.read_file('/content/gdrive/My Drive/cv capstone/stage_2_train_images/'+file_name)
patient_id = (class_metadata['patientId']==image_data.PatientID)
class_metadata.loc[patient_id,'Modality'] = image_data.Modality
class_metadata.loc[patient_id,'PatientAge'] = pd.to_numeric(image_data.PatientAge)
class_metadata.loc[patient_id,'PatientSex'] = image_data.PatientSex
class_metadata.loc[patient_id,'ViewPosition'] = image_data.ViewPosition
class_metadata.loc[patient_id,'BodyPartExamined'] = image_data.BodyPartExamined
Streaming output truncated to the last 5000 lines.
processing: 21684 45ddbd0e-006a-41c0-a32a-62a4cbeb2a13.dcm
processing: 21685 45dddc96-f7e8-4dcf-94b1-34349c0ffdc2.dcm
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processing: 26636 0996439a-352a-4c75-bb53-d13759eca6b6.dcm
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processing: 26638 08dc2e8b-754a-47ad-97e2-efe6e14fd732.dcm
processing: 26639 089df963-c3c7-499c-afd8-2b0f9224c442.dcm
processing: 26640 095edab1-09c6-4f83-931e-f0a33b77639c.dcm
processing: 26641 098c0843-d0f1-4aa3-b593-925d9327e31a.dcm
processing: 26642 0965f1e9-2905-40ef-8612-4984e2c7e9ea.dcm
processing: 26643 088b89f6-6847-4a20-b1ad-5887b56bbae7.dcm
processing: 26644 09591753-b569-435e-bd44-a80a3cfc6927.dcm
processing: 26645 0889b108-8f03-4fef-9ef3-5ddabde15ce7.dcm
processing: 26646 08c343b6-9dc4-405b-87b3-b6534abcb990.dcm
processing: 26647 08d88a60-146c-49a7-9450-45293024cf46.dcm
processing: 26648 0985fbca-8311-4a6a-9d74-7dca4dce2521.dcm
processing: 26649 0906b366-f454-4c62-88fc-caf7ffb5d36b.dcm
processing: 26650 09c0e0e5-e269-4d0c-89ea-59d1a77e2097.dcm
processing: 26651 0977f16c-c343-42c9-95ed-d7ca996feb16.dcm
processing: 26652 08d8e57a-4835-445b-8c79-8dd31ab17819.dcm
processing: 26653 08e84251-b9cc-47e7-9486-be7becdd1d84.dcm
processing: 26654 094eed38-9c5b-4042-936d-344ccec4c3cc.dcm
processing: 26655 09db0213-0ad3-4062-bc7b-9a82e5a74097.dcm
processing: 26656 08ae6c0b-d044-4de2-a410-b3cf8dc65868.dcm
processing: 26657 09ad8591-5c65-49f5-a297-3ffe33811bb5.dcm
processing: 26658 091d75a8-e23e-4cb8-af67-10cb314077ec.dcm
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processing: 26667 09653ae4-ae24-4839-a56e-7270d6e51eb7.dcm
processing: 26668 097a2839-5587-4bf4-9adb-641d8caa877b.dcm
processing: 26669 09009286-a589-4fb9-8c2c-540e16c75481.dcm
processing: 26670 09afc58a-053c-420e-b379-e331aec15a7f.dcm
processing: 26671 09854bb5-4fed-43e2-96cf-8f24ca71cb6e.dcm
processing: 26672 09e8fae9-1714-4b54-a1d3-42a29a9717c3.dcm
processing: 26673 08a40736-fee3-405f-a326-c1af30e724bc.dcm
processing: 26674 091781dd-be2b-4517-9264-ade2f0c85487.dcm
processing: 26675 09471800-38bb-448e-9a36-5544dd585676.dcm
processing: 26676 08e237d8-7d4d-410f-b48e-1e2817df9aac.dcm
processing: 26677 09d21970-3207-4817-ab35-b26bbfdead9e.dcm
processing: 26678 0976dbc4-041f-4bb0-9110-3024d0786c8b.dcm
processing: 26679 090abf67-66f4-413b-ac4a-bfad36e07e1a.dcm
processing: 26680 09875c3b-c520-4b08-89b0-a7a7299c37b2.dcm
processing: 26681 09a71d45-931c-4089-958d-3eca0f1e303a.dcm
processing: 26682 095ce5a1-3928-4120-9f15-24162c7f27c7.dcm
processing: 26683 08845a57-ec6b-4efc-b919-6020dc4460b9.dcm
In [ ]:
class_metadata.BodyPartExamined.value_counts()
Out[ ]:
CHEST 30227 Name: BodyPartExamined, dtype: int64
In [ ]:
class_metadata.Modality.value_counts()
Out[ ]:
CR 30227 Name: Modality, dtype: int64
Observation:
- "BodyPartExamined" is CHEST which is expected
- "Modality" is CR (Computer Radiography)
In [ ]:
class_metadata.drop(['Age', 'Sex', 'BodyPartExamined'], axis=1,inplace =True)
class_metadata.head()
Out[ ]:
| patientId | x | y | width | height | Target | class | Modality | PatientAge | PatientSex | ViewPosition | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 51.0 | F | PA |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 48.0 | F | PA |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 19.0 | M | AP |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal | CR | 28.0 | M | PA |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | CR | 32.0 | F | AP |
In [ ]:
picklefile = open('/content/gdrive/My Drive/cv capstone/train1', 'wb')
pickle.dump(class_metadata, picklefile)
picklefile.close()
In [ ]:
picklefile = open('/content/gdrive/My Drive/cv capstone/train1', 'rb')
training_data= pickle.load(picklefile)
picklefile.close()
In [ ]:
#Converting age to Numeric as the current data type is a String
training_data['PatientAge'] = training_data.PatientAge.astype(int)
In [ ]:
filepaths=[]
source_dir='/content/gdrive/My Drive/cv capstone/stage_2_train_images/'
In [ ]:
for f in training_data['patientId']:
fpath=os.path.join(source_dir,f)
filepaths.append(fpath)
In [ ]:
training_data['path']=filepaths
In [ ]:
training_data.sample(5)
Out[ ]:
| patientId | x | y | width | height | Target | class | Modality | PatientAge | PatientSex | ViewPosition | path | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16561 | a008f7a9-38ba-4ea8-aa6c-3337dff354d6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 63 | M | PA | /content/gdrive/My Drive/cv capstone/stage_2_t... |
| 10351 | 6e71c881-b814-457f-8d31-00bda463396e | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 69 | M | AP | /content/gdrive/My Drive/cv capstone/stage_2_t... |
| 16806 | a1f31712-6665-443c-b0f9-4fe206e16fb2 | NaN | NaN | NaN | NaN | 0 | Normal | CR | 36 | F | PA | /content/gdrive/My Drive/cv capstone/stage_2_t... |
| 23580 | d4683e9b-ee14-40e1-a9f9-1eebd8d125ee | 285.0 | 301.0 | 226.0 | 412.0 | 1 | Lung Opacity | CR | 68 | F | AP | /content/gdrive/My Drive/cv capstone/stage_2_t... |
| 16480 | 9f65dff1-52fc-4d14-b918-a931ce1f91aa | NaN | NaN | NaN | NaN | 0 | Normal | CR | 47 | F | PA | /content/gdrive/My Drive/cv capstone/stage_2_t... |
In [ ]:
import matplotlib.patches as patches
def MapImagesToAnnotations(pid): # mapping images and annotations
data = list(training_data[training_data['patientId']==pid].T.to_dict().values())
for i,dr in enumerate(data):
patientImage =dr['patientId']
target=dr['Target']
cls =dr['class']
f, ax = plt.subplots()
id =dr['path']+'.dcm'
pic = dcm.read_file(id, force =True)
modality = pic.Modality
age = pic.PatientAge
sex = pic.PatientSex
bodyPart = pic.BodyPartExamined
img= pic.pixel_array
ax.imshow(img,cmap=plt.cm.bone)
ax.set_title('ID: {} \nModality: {} Age: {} Sex: {} class: {} Target: {} BodyPartExamined: {}'.format(pid,modality,age,sex,cls,target,bodyPart))
if dr['Target']==1:
x, y, width, height = dr['x'],dr['y'],dr['width'],dr['height']
rect = patches.Rectangle((x, y),width, height,color="blue",alpha = 0.2)
ax.add_patch(rect)
plt.show()
In [ ]:
MapImagesToAnnotations('c1edf42b-5958-47ff-a1e7-4f23d99583ba') #mapping image and annotations
In [ ]:
MapImagesToAnnotations('c1f7889a-9ea9-4acb-b64c-b737c929599a') #mapping image and annotations
Step 4:Preprocessing and Visualisation of different classes [4 Points]
In [ ]:
training_data.describe(include="all").T
Out[ ]:
| count | unique | top | freq | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| patientId | 30227 | 26684 | 3239951b-6211-4290-b237-3d9ad17176db | 4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| x | 9555.0 | NaN | NaN | NaN | 394.047724 | 204.574172 | 2.0 | 207.0 | 324.0 | 594.0 | 835.0 |
| y | 9555.0 | NaN | NaN | NaN | 366.83956 | 148.940488 | 2.0 | 249.0 | 365.0 | 478.5 | 881.0 |
| width | 9555.0 | NaN | NaN | NaN | 218.471376 | 59.289475 | 40.0 | 177.0 | 217.0 | 259.0 | 528.0 |
| height | 9555.0 | NaN | NaN | NaN | 329.269702 | 157.750755 | 45.0 | 203.0 | 298.0 | 438.0 | 942.0 |
| Target | 30227.0 | NaN | NaN | NaN | 0.316108 | 0.464963 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| class | 30227 | 3 | No Lung Opacity / Not Normal | 11821 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Modality | 30227 | 1 | CR | 30227 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| PatientAge | 30227.0 | NaN | NaN | NaN | 46.797764 | 16.89294 | 1.0 | 34.0 | 49.0 | 59.0 | 155.0 |
| PatientSex | 30227 | 2 | M | 17216 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| ViewPosition | 30227 | 2 | AP | 15297 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| path | 30227 | 26684 | drive/My Drive/cv capstone/stage_2_train_image... | 4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Observation: The mean age is 46 years , where as minimum age is 1 year and the max age is 155 which seems to be an outlier
50% of the patiens are of aroudn 49 age , the std deviation is 16 which sugges that age is not normally distubuted
In [ ]:
training_data.PatientSex.value_counts()
Out[ ]:
M 17216 F 13011 Name: PatientSex, dtype: int64
In [ ]:
## Distbution of Class Among the tragets
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('Target')['class'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'class', data = data_target_class, palette = 'Set3')
plt.title('class and Target for Chest Exams')
Out[ ]:
Text(0.5, 1.0, 'class and Target for Chest Exams')
In [ ]:
## Distbution of Sex Among the tragets
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('Target')['PatientSex'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'PatientSex', data = data_target_class, palette = 'Set3')
plt.title('Sex and Target for Chest Exams')
Out[ ]:
Text(0.5, 1.0, 'Sex and Target for Chest Exams')
Observation: The number of males in both category are higher than women
In [ ]:
## Distbution of Sex Among the classes
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('class')['PatientSex'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'class', y = 'Values', hue = 'PatientSex', data = data_target_class, palette = 'Set3')
plt.title('Sex and class for Chest Exams')
Out[ ]:
Text(0.5, 1.0, 'Sex and class for Chest Exams')
Observation: The number of males in all classes are higher than women
In [ ]:
# plots the distrubution of age
sns.distplot(training_data.PatientAge)
/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f4641bfbcd0>
Observation: Looks like normal distubution of age
In [ ]:
plt.figure(figsize=(10,5)) # setting the figure size
ax = sns.barplot(x='class', y='PatientAge', data=training_data, palette='muted') # barplot'
Observation: This is the distubution of Age with class, maximum age of person with pneuomina is arund 45
In [ ]:
plt.figure(figsize=(15,7))
sns.boxplot(x='class', y='PatientAge', data= training_data)
plt.show()
Observation: The class which has no pneuomia has few outliers , theie age is somewhere around 155 years so we can clip the upper threshold to 100
In [ ]:
training_data['PatientAge'] = training_data['PatientAge'].clip(training_data['PatientAge'].min(), 100)
In [ ]:
temp = training_data
print('Number of patients in age category ')
temp['CategoryAge'] = pd.cut(temp['PatientAge'], bins = 4, precision = 0, labels = ['<=26', '<=50', '<=75', '<=100'])
temp['CategoryAge'].value_counts()
Number of patients in age category
Out[ ]:
<=75 13318 <=50 12157 <=26 3972 <=100 780 Name: CategoryAge, dtype: int64
In [ ]:
label_count=temp['CategoryAge'].value_counts()
explode = (0.01,0.01,0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('CategoryAge Distribution Overall')
plt.show()
In [ ]:
label_count=temp.loc[temp['Target'] == 1,'CategoryAge'].value_counts()
explode = (0.01,0.01,0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('CategoryAge Distribution with Pneumonia evidence')
plt.show()
Observation:
- Number of patients are highest for age group 51-75 for overall case
- But number of patients are highest for age group 27-50 for Target=1 case.
In [ ]:
training_data['class'].value_counts()
Out[ ]:
No Lung Opacity / Not Normal 11821 Lung Opacity 9555 Normal 8851 Name: class, dtype: int64
Observation:
- There are 8851 normal cases , person with lung opactiry are 9555 and No Lung Opacity / Not Normal are 11821
- Patients with evidence of Pneumonia are associated with Lung Opacity class and target = 1.
- Patients with no definitive effidence of Pneumonia are either of Normal or No Lung Opacity / Not Normal class and target = 0.
In [ ]:
label_count=training_data['class'].value_counts()
explode = (0.01,0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Class Distribution')
plt.show()
In [ ]:
label_count=training_data['Target'].value_counts()
explode = (0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Class Distribution')
plt.show()
- Posterior/Anterior (PA): X-ray is taken from back part of chest. So it hits posterior part before anterior part. Patient needs to stand against X-ray machine.
- Anterior/Posterior (AP): X-ray is taken from front part of chest. So it hits anterior part before posterior part. When Patient cannot stand against X-ray machine. this is taken but heart size is exaggerated.
In [ ]:
label_count=training_data['ViewPosition'].value_counts()
explode = (0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('ViewPosition Distribution Overall')
plt.show()
In [ ]:
label_count=training_data.loc[training_data['Target'] == 1,'ViewPosition'].value_counts()
explode = (0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('ViewPosition Distribution with Pneumonia evidence')
plt.show()
Observation:
- ViewPosition = PA and ViewPosition = AP are almost equally distributed in the overall train data
- When Target=1, View position = AP dominates.
In [ ]:
# Distbution of Viewposition Among the tragets
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('Target')['ViewPosition'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'ViewPosition', data = data_target_class, palette = 'Set3')
plt.title('View Positon and Target for Chest Exams')
Out[ ]:
Text(0.5, 1.0, 'View Positon and Target for Chest Exams')
In [ ]:
# Distbution of Viewposition Among the classes
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('class')['ViewPosition'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'class', y = 'Values', hue = 'ViewPosition', data = data_target_class, palette = 'Set3')
plt.title('View Positon and class for Chest Exams')
Out[ ]:
Text(0.5, 1.0, 'View Positon and class for Chest Exams')
In [ ]:
from matplotlib.patches import Rectangle
def plot_diagram(train_data, pnemonia_point_color, chest_image_color,text):
fig, ax = plt.subplots(1,1,figsize=(10,10))
plt.title("Pneumonia presence points on the chest image\n{}".format(text))
train_data.plot.scatter(x='xc', y='yc', xlim=(0,1024), ylim=(0,1024), ax=ax, alpha=0.8, marker=".", color=pnemonia_point_color)
for i, train_row in train_data.iterrows():
ax.add_patch(Rectangle(xy=(train_row['x'], train_row['y']),
width=train_row['width'],height=train_row['height'],alpha=3.5e-3, color=chest_image_color))
plt.show()
In [ ]:
temp = training_data
temp['xc'] = temp['x'] + temp['width'] / 2
temp['yc'] = temp['y'] + temp['height'] / 2
rows = (temp['ViewPosition']=='AP') & (temp['Target']==1)
plot_diagram(temp[ rows ],'green', 'yellow', 'View Position: AP')
In [ ]:
rows = (temp['ViewPosition']=='PA') & (temp['Target']==1)
plot_diagram(temp[ rows ],'green', 'yellow', 'View Position: PA')
In [ ]:
training_data.drop(['xc', 'yc','CategoryAge'], axis=1,inplace =True) #dropping unecessary columns now
In [ ]:
bounding_boxes = training_data[training_data['Target'] == 1]
bounding_boxes = bounding_boxes.groupby('patientId').size().to_frame('number_of_bounding_boxes').reset_index()
train_labels = training_data.merge(bounding_boxes, on = 'patientId', how = 'left')
print('Number of patients per bounding boxes in the dataset')
(bounding_boxes.groupby('number_of_bounding_boxes').size().to_frame('number_of_patients_per_bounding_boxes').reset_index().set_index('number_of_bounding_boxes').sort_values(by = 'number_of_bounding_boxes'))
Number of patients per bounding boxes in the dataset
Out[ ]:
| number_of_patients_per_bounding_boxes | |
|---|---|
| number_of_bounding_boxes | |
| 1 | 2614 |
| 2 | 3266 |
| 3 | 119 |
| 4 | 13 |
In [ ]:
train_labels.head()
Out[ ]:
| patientId | x | y | width | height | Target | class | Modality | PatientAge | PatientSex | ViewPosition | path | number_of_bounding_boxes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 51 | F | PA | drive/My Drive/cv capstone/stage_2_train_image... | NaN |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 48 | F | PA | drive/My Drive/cv capstone/stage_2_train_image... | NaN |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 19 | M | AP | drive/My Drive/cv capstone/stage_2_train_image... | NaN |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal | CR | 28 | M | PA | drive/My Drive/cv capstone/stage_2_train_image... | NaN |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | CR | 32 | F | AP | drive/My Drive/cv capstone/stage_2_train_image... | 2.0 |
In [ ]:
train_labels[train_labels['patientId'] == '00436515-870c-4b36-a041-de91049b9ab4']
Out[ ]:
| patientId | x | y | width | height | Target | class | Modality | PatientAge | PatientSex | ViewPosition | path | number_of_bounding_boxes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | CR | 32 | F | AP | drive/My Drive/cv capstone/stage_2_train_image... | 2.0 |
| 5 | 00436515-870c-4b36-a041-de91049b9ab4 | 562.0 | 152.0 | 256.0 | 453.0 | 1 | Lung Opacity | CR | 32 | F | AP | drive/My Drive/cv capstone/stage_2_train_image... | 2.0 |
Observations: Maximum patients have 2 bounding boxes while 13 patients have 4 bounding boxes.
Disturbution of Classes, there are 39% of patients with No Lung opacity , 29.3% Normal
Step 5: Display images with bounding box. [ 5 points ]
In [ ]:
from matplotlib.patches import Rectangle
def boundbox(data):
image_data = list(data.T.to_dict().values())
f, axes = plt.subplots(2,2, figsize=(16,18))
for index, patient_info in enumerate(image_data):
lungs_image = patient_info['patientId']+'.dcm'
lungs_image_data = dcm.read_file('/content/gdrive/My Drive/cv capstone/stage_2_train_images/'+lungs_image)
axes[index//2, index%2].imshow(lungs_image_data.pixel_array, cmap=plt.cm.gray)
axes[index//2, index%2].set_title('ID: {}\nModality: {} Age: {} Sex: {} Target: {}\nClass: {}'.format(
patient_info['patientId'],lungs_image_data.Modality, lungs_image_data.PatientAge, lungs_image_data.PatientSex, patient_info['Target'], patient_info['class']))
matched_rows = training_data[training_data['patientId']==patient_info['patientId']]
bounding_box = list(matched_rows.T.to_dict().values())
for j, row in enumerate(bounding_box):
axes[index//2, index%2].add_patch(Rectangle(xy=(row['x'], row['y']),width=row['width'],height=row['height'], color="blue",alpha = 0.1))
plt.show()
X-ray images with ONE bounding box
In [ ]:
data = train_labels[train_labels['number_of_bounding_boxes']==1].sample(4)
boundbox(data)
X-ray images with TWO bounding box
In [ ]:
data = train_labels[train_labels['number_of_bounding_boxes']==2].sample(4)
boundbox(data)
X-ray images with THREE bounding box
In [ ]:
data = train_labels[train_labels['number_of_bounding_boxes']==3].sample(4)
boundbox(data)
X-ray images with FOUR bounding box
In [ ]:
data = train_labels[train_labels['number_of_bounding_boxes']==4].sample(4)
boundbox(data)
X-ray images with TARGET=1
In [ ]:
data = training_data[training_data['Target']==1].sample(4) #target is 1
boundbox(data)
X-ray images with TARGET=0 => no bounding box
In [ ]:
data1 = training_data[training_data['Target']==0].sample(4) #target is 0
boundbox(data1)
Step 6: Design, train and test basic CNN models for classification. [ 10 points]
In [ ]:
training_data.head()
Out[ ]:
| patientId | x | y | width | height | Target | class | Modality | PatientAge | PatientSex | ViewPosition | path | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 51 | F | PA | drive/My Drive/cv capstone/stage_2_train_image... |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 48 | F | PA | drive/My Drive/cv capstone/stage_2_train_image... |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal | CR | 19 | M | AP | drive/My Drive/cv capstone/stage_2_train_image... |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal | CR | 28 | M | PA | drive/My Drive/cv capstone/stage_2_train_image... |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity | CR | 32 | F | AP | drive/My Drive/cv capstone/stage_2_train_image... |
In [ ]:
model_trainigdata = training_data.groupby('Target', group_keys=False).apply(lambda x: x.sample(7000))
In [ ]:
model_trainigdata["Target"].value_counts()
Out[ ]:
0 7000 1 7000 Name: Target, dtype: int64
In [ ]:
from tqdm import tqdm_notebook
def resizeImage(row):
image_data_list = [];
target_list = [];
for index, row in tqdm_notebook(row.iterrows()):
patientId = row.patientId
target = row["Target"]
dcm_file = '/content/gdrive/My Drive/cv capstone/stage_2_train_images/' + '{}.dcm'.format(patientId)
image_data_array = dcm.read_file(dcm_file).pixel_array
if len(image_data_array.shape) != 3 or image_data_array.shape[2] != 3:
image_data_array = np.stack((image_data_array,) * 3, -1)
image_array = np.array(image_data_array).astype(np.uint8)
image_data_array = cv2.resize(image_array, (NEW_IMAGE_SIZE, NEW_IMAGE_SIZE), interpolation = cv2.INTER_LINEAR)
image_data_list.append(image_data_array)
target_list.append(target)
return image_data_list, target_list
In [ ]:
imageData, targets = resizeImage(model_trainigdata)
imageData = np.array(imageData)
targets = np.array(targets)
<ipython-input-51-0c5e939fc742>:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0 Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook` for index, row in tqdm_notebook(row.iterrows()):
In [ ]:
#create a file
picklefile = open('/content/gdrive/My Drive/cv capstone/xtrain1', 'wb')
picklefile1 = open('/content/gdrive/My Drive/cv capstone/y1', 'wb')
#pickle the dataframe
pickle.dump(imageData, picklefile)
pickle.dump(targets, picklefile1)
#close file
picklefile.close()
picklefile1.close()
In [ ]:
picklefile = open('/content/gdrive/My Drive/cv capstone/xtrain1', 'rb')
picklefile1 = open('/content/gdrive/My Drive/cv capstone/y1', 'rb')
#unpickle the dataframe
imgs= pickle.load(picklefile)
lbl= pickle.load(picklefile1)
#close file
picklefile.close()
picklefile1.close()
#print the dataframe
print(type(imgs))
print(type(lbl))
<class 'list'> <class 'list'>
In [ ]:
imgs = np.array(imgs)
lbl = np.array(lbl)
In [ ]:
print(lbl.shape)
print(imgs.shape)
(14000,) (14000, 128, 128, 3)
In [ ]:
from sklearn.preprocessing import LabelBinarizer
enc = LabelBinarizer()
encoded_targets = enc.fit_transform(lbl)
In [ ]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(imgs, encoded_targets, test_size=0.3, random_state=50)
X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=50)
In [ ]:
NEW_IMAGE_SIZE = 128
EPOCH = 10
NUM_CLASSES = 1
NUM_CHANNELS = 3
In [ ]:
def buildBasicModel(loss='binary_crossentropy', metrics=['accuracy']):
shape_input = (128,128,3)
model = Sequential()
model.add(Conv2D(filters = 32,kernel_size=(5,5), padding = 'Same', activation='relu',input_shape=shape_input))
model.add(Conv2D(filters = 32,kernel_size=(5,5), padding = 'Same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(filters = 64,kernel_size=(4,4), padding = 'Same', activation='relu'))
model.add(Conv2D(filters = 64,kernel_size=(4,4), padding = 'Same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.3))
model.add(Conv2D(filters = 128,kernel_size=(3,3), padding = 'Same', activation='relu'))
model.add(Conv2D(filters = 128,kernel_size=(3,3), padding = 'Same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.4))
model.add(GlobalMaxPooling2D())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(NUM_CLASSES, activation = "softmax"))
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
model.summary()
return model
In [ ]:
model = buildBasicModel()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 128, 128, 32) 2432
conv2d_2 (Conv2D) (None, 128, 128, 32) 25632
max_pooling2d (MaxPooling2D (None, 64, 64, 32) 0
)
dropout_1 (Dropout) (None, 64, 64, 32) 0
conv2d_3 (Conv2D) (None, 64, 64, 64) 32832
conv2d_4 (Conv2D) (None, 64, 64, 64) 65600
max_pooling2d_1 (MaxPooling (None, 32, 32, 64) 0
2D)
dropout_2 (Dropout) (None, 32, 32, 64) 0
conv2d_5 (Conv2D) (None, 32, 32, 128) 73856
conv2d_6 (Conv2D) (None, 32, 32, 128) 147584
max_pooling2d_2 (MaxPooling (None, 16, 16, 128) 0
2D)
dropout_3 (Dropout) (None, 16, 16, 128) 0
global_max_pooling2d (Globa (None, 128) 0
lMaxPooling2D)
dense_1 (Dense) (None, 64) 8256
dropout_4 (Dropout) (None, 64) 0
dense_2 (Dense) (None, 1) 65
=================================================================
Total params: 356,257
Trainable params: 356,257
Non-trainable params: 0
_________________________________________________________________
In [ ]:
history = model.fit(X_train, y_train, epochs=EPOCH, validation_data=(X_val, y_val), batch_size = 32)
Epoch 1/10 307/307 [==============================] - 24s 70ms/step - loss: 0.8028 - accuracy: 0.5002 - val_loss: 0.6633 - val_accuracy: 0.4919 Epoch 2/10 307/307 [==============================] - 21s 69ms/step - loss: 0.6490 - accuracy: 0.5002 - val_loss: 0.6525 - val_accuracy: 0.4919 Epoch 3/10 307/307 [==============================] - 21s 67ms/step - loss: 0.6383 - accuracy: 0.5002 - val_loss: 0.6547 - val_accuracy: 0.4919 Epoch 4/10 307/307 [==============================] - 21s 68ms/step - loss: 0.6314 - accuracy: 0.5002 - val_loss: 0.6377 - val_accuracy: 0.4919 Epoch 5/10 307/307 [==============================] - 21s 68ms/step - loss: 0.6264 - accuracy: 0.5002 - val_loss: 0.6402 - val_accuracy: 0.4919 Epoch 6/10 307/307 [==============================] - 21s 68ms/step - loss: 0.6192 - accuracy: 0.5002 - val_loss: 0.6361 - val_accuracy: 0.4919 Epoch 7/10 307/307 [==============================] - 21s 68ms/step - loss: 0.6125 - accuracy: 0.5002 - val_loss: 0.6169 - val_accuracy: 0.4919 Epoch 8/10 307/307 [==============================] - 21s 67ms/step - loss: 0.6058 - accuracy: 0.5002 - val_loss: 0.6260 - val_accuracy: 0.4919 Epoch 9/10 307/307 [==============================] - 21s 67ms/step - loss: 0.6064 - accuracy: 0.5002 - val_loss: 0.6124 - val_accuracy: 0.4919 Epoch 10/10 307/307 [==============================] - 21s 67ms/step - loss: 0.6061 - accuracy: 0.5002 - val_loss: 0.6164 - val_accuracy: 0.4919
In [ ]:
loss, accuracy = model.evaluate(X_train, y_train, verbose = 1)
print("Train loss:", loss)
print("Train_Accuracy:", accuracy)
307/307 [==============================] - 6s 21ms/step - loss: 0.6046 - accuracy: 0.5002 Train loss: 0.6045503616333008 Train_Accuracy: 0.5002040863037109
In [ ]:
loss, accuracy = model.evaluate(X_test, y_test, verbose = 1)
print("Test loss:", loss)
print("Test_Accuracy:", accuracy)
66/66 [==============================] - 1s 21ms/step - loss: 0.6285 - accuracy: 0.5071 Test loss: 0.6284589767456055 Test_Accuracy: 0.5071428418159485
In [ ]:
y_pred=(model.predict(X_test) > 0.5).astype("int32")
print('Confusion Matrix:\n',confusion_matrix(y_test,y_pred),'\n')
66/66 [==============================] - 1s 19ms/step Confusion Matrix: [[ 0 1035] [ 0 1065]]
In [ ]:
print('Classification Report:\n',classification_report(y_test,y_pred),'\n')
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 1035
1 0.51 1.00 0.67 1065
accuracy 0.51 2100
macro avg 0.25 0.50 0.34 2100
weighted avg 0.26 0.51 0.34 2100
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
In [ ]:
print('Accuracy Score:\n',accuracy_score(y_test,y_pred),'\n')
Accuracy Score: 0.5071428571428571
In [ ]:
eval=model.evaluate(X_test,y_test)
print('Model Evaluation Loss :\n',eval[0])
print('Model Evaluation Accuracy :\n',eval[1])
66/66 [==============================] - 1s 21ms/step - loss: 0.6285 - accuracy: 0.5071 Model Evaluation Loss : 0.6284589767456055 Model Evaluation Accuracy : 0.5071428418159485
In [ ]:
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout, Flatten, Input,GlobalAveragePooling2D
def build_model():
input_shape = (NEW_IMAGE_SIZE,NEW_IMAGE_SIZE,NUM_CHANNELS)
inputs = Input(shape = input_shape)
initializer = tf.keras.initializers.GlorotNormal()
denseNetModel = DenseNet121(include_top = False, input_tensor = inputs, weights ='imagenet')
densenet = Model(inputs = inputs, outputs = denseNetModel.layers[-1].output, name = 'DenseNet121')
model = Sequential(name = 'DenseNet121')
model.add(densenet)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.4))
model.add(Dense(1, activation = 'sigmoid', kernel_initializer = initializer))
model.summary()
return model
In [ ]:
model = build_model()
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5
29084464/29084464 [==============================] - 2s 0us/step
Model: "DenseNet121"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
DenseNet121 (Functional) (None, 4, 4, 1024) 7037504
global_average_pooling2d (G (None, 1024) 0
lobalAveragePooling2D)
dropout (Dropout) (None, 1024) 0
dense (Dense) (None, 1) 1025
=================================================================
Total params: 7,038,529
Trainable params: 6,954,881
Non-trainable params: 83,648
_________________________________________________________________
In [ ]:
import tensorflow as tf
def cosine_annealing(x):
lr = 0.001
epochs = 25
return lr* (np.cos(np.pi*x/epochs)+1.) /2
learning_rate = tf.keras.callbacks.LearningRateScheduler(cosine_annealing)
early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0.005, patience=4, restore_best_weights=True, verbose=1, mode='auto')
In [ ]:
from tensorflow.keras.optimizers import Adam
LEARNING_RATE = 1e-4
LOSS = 'binary_crossentropy'
METRICS = ['accuracy']
model.compile(optimizer = Adam(lr = LEARNING_RATE), loss = LOSS, metrics = METRICS)
/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead. super(Adam, self).__init__(name, **kwargs)
In [ ]:
history=model.fit(X_train,y_train,
validation_data=(X_val,y_val),
callbacks=[learning_rate,early_stopping],
epochs=EPOCH,
batch_size=32)
Epoch 1/10 307/307 [==============================] - 68s 149ms/step - loss: 0.5473 - accuracy: 0.7564 - val_loss: 1.0293 - val_accuracy: 0.6457 - lr: 0.0010 Epoch 2/10 307/307 [==============================] - 41s 134ms/step - loss: 0.4705 - accuracy: 0.7884 - val_loss: 0.5611 - val_accuracy: 0.7676 - lr: 9.9606e-04 Epoch 3/10 307/307 [==============================] - 42s 136ms/step - loss: 0.4478 - accuracy: 0.7990 - val_loss: 1.0782 - val_accuracy: 0.4919 - lr: 9.8429e-04 Epoch 4/10 307/307 [==============================] - 42s 136ms/step - loss: 0.4346 - accuracy: 0.8013 - val_loss: 0.5012 - val_accuracy: 0.7648 - lr: 9.6489e-04 Epoch 5/10 307/307 [==============================] - 41s 135ms/step - loss: 0.4259 - accuracy: 0.8049 - val_loss: 0.6473 - val_accuracy: 0.7314 - lr: 9.3815e-04 Epoch 6/10 307/307 [==============================] - 42s 138ms/step - loss: 0.4218 - accuracy: 0.8061 - val_loss: 0.4437 - val_accuracy: 0.7857 - lr: 9.0451e-04 Epoch 7/10 307/307 [==============================] - 45s 145ms/step - loss: 0.3987 - accuracy: 0.8210 - val_loss: 0.4893 - val_accuracy: 0.7805 - lr: 8.6448e-04 Epoch 8/10 307/307 [==============================] - 41s 134ms/step - loss: 0.3819 - accuracy: 0.8309 - val_loss: 0.5248 - val_accuracy: 0.7186 - lr: 8.1871e-04 Epoch 9/10 307/307 [==============================] - 43s 141ms/step - loss: 0.3625 - accuracy: 0.8427 - val_loss: 0.4781 - val_accuracy: 0.7876 - lr: 7.6791e-04 Epoch 10/10 307/307 [==============================] - ETA: 0s - loss: 0.3382 - accuracy: 0.8539Restoring model weights from the end of the best epoch: 6. 307/307 [==============================] - 43s 140ms/step - loss: 0.3382 - accuracy: 0.8539 - val_loss: 0.4511 - val_accuracy: 0.8010 - lr: 7.1289e-04 Epoch 10: early stopping
In [ ]:
y_pred=(model.predict(X_test) > 0.5).astype("int32")
print('Confusion Matrix:\n',confusion_matrix(y_test,y_pred),'\n')
66/66 [==============================] - 4s 33ms/step Confusion Matrix: [[844 191] [254 811]]
In [ ]:
print('Classification Report:\n',classification_report(y_test,y_pred),'\n')
Classification Report:
precision recall f1-score support
0 0.77 0.82 0.79 1035
1 0.81 0.76 0.78 1065
accuracy 0.79 2100
macro avg 0.79 0.79 0.79 2100
weighted avg 0.79 0.79 0.79 2100
In [ ]:
print('Accuracy Score:\n',accuracy_score(y_test,y_pred),'\n')
Accuracy Score: 0.7880952380952381
In [ ]:
eval=model.evaluate(X_test,y_test,verbose=0)
print('Model Evaluation Loss :\n',eval[0])
print('Model Evaluation Accuracy :\n',eval[1])
Model Evaluation Loss : 0.468567430973053 Model Evaluation Accuracy : 0.788095235824585
In [ ]:
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train loss', 'Validation loss'], loc='best')
plt.show()
In [ ]:
plt.imshow(X_test[1], cmap ='gray')
y_pred = model.predict(X_test)
output=(model.predict(X_test) > 0.5).astype("int32")
print("Actual label:", y_test[1])
print("Predicted label:", output[1])
print("Model output:", y_pred[1])
66/66 [==============================] - 2s 32ms/step 66/66 [==============================] - 2s 32ms/step Actual label: [0] Predicted label: [0] Model output: [0.45136756]
